A cooperative control framework for haptic guidance of bimanual surgical tasks based on Learning From Demonstration

Abstract

Whilst current minimally invasive surgical robots offer many advantages to the surgeon, most of them are still controlled using the traditional master-slave approach, without fully exploiting the complementary strengths of both the human user and the robot. This paper proposes a framework that provides a cooperative control approach to human-robot interaction. Typical teleoperation is enhanced by incorporating haptic guidance-based feedback for surgical tasks, which are demonstrated to and learned by the robot. Safety in the surgical scene is maintained during reproduction of the learned tasks by including the surgeon in the guided execution of the learned task at all times. Continuous Hidden Markov Models are used for task learning, real-time learned task recognition and generating setpoint trajectories for haptic guidance. Two different surgical training tasks were demonstrated and encoded by the system, and the framework was evaluated using the Raven II surgical robot research platform. The results indicate an improvement in user task performance with the haptic guidance in comparison to unguided teleoperation.